Kernel Approach for Modeling Interaction Effects in Genetic Association Studies of Complex Quantitative Traits

• Type of Material: Article
• Authors: K. Alaine Broadaway, Emory University; Richard Duncan, Emory University; Karen Conneely, Emory University; Lynn Almli, Emory University; Bekh Bradley-Davino, Emory University; Kerry Ressler, Emory University; Michael Epstein, Emory University
• Language: English
• Date: 2015-07-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2015 WILEY PERIODICALS, INC.
• Final Published Version (URL): http://dx.doi.org/10.1002/gepi.21901
• Title of Journal or Parent Work: Genetic Epidemiology
• ISSN: 0741-0395
• Volume: 39
• Issue: 5
• Start Page: 366
• End Page: 375
• Grant/Funding Information: This work was support by NIH R01-HG007508 from the National Human Genome Research Institute R01-MH071537 from the National Institute of Mental Health, and National Institute of Arthritis and Musculoseketal and Skin Diseases R01-AR060893.
• Keywords: ENVIRONMENT INTERACTION; GENOME-WIDE ASSOCIATION; BLOOD-PRESSURE; MODERATION; TESTS; Life Sciences & Biomedicine; POLYMORPHISM; MARKER-SET; gene mapping; quantitative human traits; gene-environment interaction; Mathematical & Computational Biology; Science & Technology; REGRESSION; POSTTRAUMATIC-STRESS-DISORDER; GWAS; Genetics & Heredity; DEPRESSION
• Research Categories: Biology, Genetics

Abstract

The etiology of complex traits likely involves the effects of genetic and environmental factors, along with complicated interaction effects between them. Consequently, there has been interest in applying genetic association tests of complex traits that account for potential modification of the genetic effect in the presence of an environmental factor. One can perform such an analysis using a joint test of gene and gene-environment interaction. An optimal joint test would be one that remains powerful under a variety of models ranging from those of strong gene-environment interaction effect to those of little or no gene-environment interaction effect. To fill this demand, we have extended a kernel machine based approach for association mapping of multiple SNPs to consider joint tests of gene and gene-environment interaction. The kernel-based approach for joint testing is promising, because it incorporates linkage disequilibrium information from multiple SNPs simultaneously in analysis and permits flexible modeling of interaction effects. Using simulated data, we show that our kernel machine approach typically outperforms the traditional joint test under strong gene-environment interaction models and further outperforms the traditional main-effect association test under models of weak or no gene-environment interaction effects. We illustrate our test using genome-wide association data from the Grady Trauma Project, a cohort of highly traumatized, at-risk individuals, which has previously been investigated for interaction effects.